store = {}
store['args']={'batch_size': 64, 'scoring_batch_size': 1000, 'test_batch_size': 1000, 'validation_set_size': 1000, 'early_stopping_patience': 5, 'epochs': 30, 'epoch_samples': 5056, 'num_inference_samples': 20, 'available_sample_k': 10, 'num_iterations': 100, 'no_cuda': False, 'name': 'bald_20_132344', 'seed': 132344, 'log_interval': 10, 'type': 'AcquisitionFunction.bald'}
store['iterations']=[]
store['initial_samples']=[14019, 33228, 20760, 10897, 16403, 42019, 56297, 45260, 29523, 51712, 31455, 45872, 55382, 27750, 36134, 51532, 15979, 51242, 18996, 10361]
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.699, 'nll': 1.9977417945861817}, 'chosen_samples': [27898, 32811, 13417, 23734, 239, 12035, 13827, 43803, 42669, 8326], 'chosen_samples_score': ['1.1893775', '1.190559', '1.2004676', '1.2021718', '1.2684553', '1.2552162', '1.2007556', '1.2019644', '1.2007817', '1.2257171']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.7176, 'nll': 1.6800496578216553}, 'chosen_samples': [31959, 19545, 36398, 38898, 50223, 9506, 32221, 38571, 16043, 12919], 'chosen_samples_score': ['1.0481863', '1.0523722', '1.053993', '1.1419793', '1.1573877', '1.0744411', '1.0872831', '1.0706298', '1.1211277', '1.1286478']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.7493, 'nll': 1.4593921899795532}, 'chosen_samples': [14851, 55329, 32409, 9433, 54066, 59449, 34445, 3992, 10871, 26223], 'chosen_samples_score': ['1.0524135', '1.0548956', '1.0749559', '1.0961415', '1.09588', '1.0590245', '1.0738792', '1.1062309', '1.0862226', '1.1199615']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8079, 'nll': 1.0198114037513732}, 'chosen_samples': [34608, 36950, 19187, 42678, 23104, 34509, 44882, 8668, 44926, 16170], 'chosen_samples_score': ['0.92513454', '0.9277997', '0.92930794', '0.9441331', '0.9476121', '0.92961735', '0.9791691', '1.0117779', '1.0784391', '1.0061152']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.7569, 'nll': 1.2105403125286103}, 'chosen_samples': [3280, 16473, 57632, 7879, 49354, 20840, 37794, 27318, 48748, 5999], 'chosen_samples_score': ['0.8556607', '0.8595509', '0.8634303', '0.8654815', '1.1087348', '0.87655085', '0.93892145', '0.94059944', '0.89739233', '0.9441848']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.7827, 'nll': 1.080329132080078}, 'chosen_samples': [42802, 8116, 28304, 15879, 53130, 45048, 8268, 4480, 28268, 28536], 'chosen_samples_score': ['0.82664037', '0.84266496', '0.89904416', '0.8646234', '0.84824705', '0.8627862', '0.84203106', '0.85902715', '0.902055', '0.85727704']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8318, 'nll': 0.9115837574005127}, 'chosen_samples': [17955, 17419, 19318, 43707, 38397, 50763, 17007, 13056, 41744, 24315], 'chosen_samples_score': ['0.86336744', '0.86629236', '0.86634463', '0.8673253', '0.9353554', '0.88376164', '0.86931634', '0.908179', '0.9356064', '0.93198824']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.8481, 'nll': 0.86854208111763}, 'chosen_samples': [11720, 44952, 5295, 20171, 33074, 57334, 48752, 13263, 33593, 28102], 'chosen_samples_score': ['0.9583141', '0.9591381', '0.976779', '0.96099263', '0.9838292', '1.0350449', '1.006247', '0.9853572', '1.0370628', '1.1476004']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.8781, 'nll': 0.8035565972328186}, 'chosen_samples': [39405, 20855, 1526, 8110, 8006, 1931, 18042, 14749, 54656, 58413], 'chosen_samples_score': ['1.0498409', '1.053288', '1.0592837', '1.0656037', '1.072187', '1.0947869', '1.0729771', '1.093623', '1.0913322', '1.090031']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.8955, 'nll': 0.647796830534935}, 'chosen_samples': [59747, 25920, 51395, 31304, 2184, 26178, 8235, 30889, 28152, 27448], 'chosen_samples_score': ['0.9181548', '0.9300487', '1.06822', '0.9638126', '1.1298598', '0.94448596', '0.93896043', '0.9301028', '0.93357384', '0.9403447']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.8936, 'nll': 0.6251121580600738}, 'chosen_samples': [25186, 15679, 46088, 38760, 7768, 52688, 20476, 12345, 24577, 40942], 'chosen_samples_score': ['0.86444193', '0.86547333', '0.86989176', '0.8734032', '0.9096193', '0.8828415', '0.9280258', '0.8943737', '0.9322649', '0.8871935']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.915, 'nll': 0.5608901709318161}, 'chosen_samples': [27435, 26444, 17958, 22518, 48767, 11693, 1518, 30742, 54801, 34540], 'chosen_samples_score': ['1.0010657', '1.0128293', '1.0354228', '1.0612249', '1.0396477', '1.0527195', '1.0640141', '1.1073855', '1.0662296', '1.0952227']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9191, 'nll': 0.5567067950963974}, 'chosen_samples': [41266, 30474, 33760, 24426, 376, 16209, 1239, 13294, 28512, 42787], 'chosen_samples_score': ['0.9690191', '0.97250736', '0.9789055', '0.9850848', '1.0820742', '1.0229208', '0.9859514', '1.0187147', '0.98643535', '1.0345386']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9205, 'nll': 0.5164340227842331}, 'chosen_samples': [57628, 14994, 40589, 38061, 19412, 43042, 36744, 27458, 8450, 30688], 'chosen_samples_score': ['0.9045043', '0.9055892', '0.90795714', '0.92930037', '0.956517', '0.941881', '1.1001153', '0.99099165', '0.96637267', '0.9198387']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9186, 'nll': 0.5015122503042221}, 'chosen_samples': [22866, 32918, 24038, 43942, 57668, 44172, 25741, 7005, 52582, 14623], 'chosen_samples_score': ['0.89470977', '0.91073513', '0.9063861', '1.0045123', '0.9333585', '0.91269714', '0.9503251', '0.9155057', '0.91109216', '0.9985257']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9243, 'nll': 0.5485123872756958}, 'chosen_samples': [37289, 42415, 46139, 41205, 17941, 24589, 6428, 1597, 44125, 13942], 'chosen_samples_score': ['1.0611744', '1.0675181', '1.1068829', '1.0683172', '1.0668154', '1.1621192', '1.0661145', '1.0906255', '1.122184', '1.0785751']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9331, 'nll': 0.4614729046821594}, 'chosen_samples': [8887, 51200, 60, 8714, 9428, 34481, 29721, 59759, 51759, 16756], 'chosen_samples_score': ['0.97410375', '0.9745178', '0.98060995', '0.9992917', '0.9854275', '1.0042965', '1.0073373', '1.0142168', '1.1294545', '1.1144483']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9405, 'nll': 0.4310338109731674}, 'chosen_samples': [18473, 26733, 52937, 27514, 24092, 32173, 3470, 44128, 12126, 18398], 'chosen_samples_score': ['1.0028464', '1.0323555', '1.0363063', '1.021781', '1.0369877', '1.0421929', '1.0390618', '1.0437937', '1.1109966', '1.0602584']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9429, 'nll': 0.40810024440288545}, 'chosen_samples': [39411, 30884, 12305, 22272, 17712, 1674, 31345, 25508, 4153, 59783], 'chosen_samples_score': ['0.92774445', '0.95779717', '0.95913917', '0.97689956', '0.9910477', '0.991578', '0.9953874', '1.0345068', '1.0118735', '1.0163252']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9508, 'nll': 0.3860275253653526}, 'chosen_samples': [52294, 49545, 59314, 50562, 8207, 37078, 27358, 28362, 52686, 13259], 'chosen_samples_score': ['1.0028942', '1.0147696', '1.0214863', '1.0225224', '1.0270028', '1.0351924', '1.0383389', '1.0551388', '1.059212', '1.0960648']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9487, 'nll': 0.40622728914022443}, 'chosen_samples': [50320, 2715, 29530, 22625, 31941, 20110, 35232, 31817, 3355, 15184], 'chosen_samples_score': ['0.9932633', '0.99727166', '0.99535304', '0.9978533', '1.0038807', '1.0793114', '1.0218711', '1.0268753', '1.0732672', '1.0329707']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9503, 'nll': 0.38496902137994765}, 'chosen_samples': [14062, 18487, 41016, 37008, 48360, 53872, 27646, 47568, 32774, 35864], 'chosen_samples_score': ['0.9244108', '0.9335882', '0.936528', '0.93872774', '0.9998733', '0.955549', '0.9779631', '1.0400746', '0.95406145', '0.97792596']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9519, 'nll': 0.3804643899202347}, 'chosen_samples': [8731, 13149, 35996, 45056, 8458, 52462, 59980, 5790, 32323, 37373], 'chosen_samples_score': ['0.91948986', '0.9293287', '0.9315274', '0.976789', '1.0370852', '0.9385508', '0.96478176', '0.98493713', '0.9858024', '1.0638545']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9545, 'nll': 0.3514158293604851}, 'chosen_samples': [29132, 18598, 12066, 32453, 27406, 32776, 44624, 22320, 16834, 36818], 'chosen_samples_score': ['0.99048305', '0.9971369', '1.0040269', '1.0457332', '1.0587596', '1.0256944', '1.010966', '1.0048678', '1.0395154', '1.1476746']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.964, 'nll': 0.3216040566563606}, 'chosen_samples': [6269, 34920, 15771, 28399, 42866, 24424, 32735, 7192, 20037, 4638], 'chosen_samples_score': ['0.94170344', '0.9431269', '0.9516492', '0.9542464', '0.9543595', '0.9815336', '0.9803049', '1.097384', '0.9582863', '0.9902015']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9577, 'nll': 0.3582372024655342}, 'chosen_samples': [52646, 12650, 39363, 8654, 38171, 10312, 54195, 42703, 10210, 23927], 'chosen_samples_score': ['0.9793293', '1.0065676', '1.0344511', '0.99095017', '0.99142367', '1.0337285', '0.9797028', '1.0366474', '1.1560816', '1.0735642']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9651, 'nll': 0.31492997258901595}, 'chosen_samples': [17698, 29938, 46412, 24940, 38698, 38165, 34520, 9588, 7325, 56082], 'chosen_samples_score': ['0.957874', '0.9761279', '0.9780036', '1.0109403', '1.0011158', '0.98040664', '1.0700762', '1.0394375', '1.1251535', '1.0670409']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9597, 'nll': 0.34597811549901963}, 'chosen_samples': [43702, 21899, 274, 18031, 13374, 52953, 52237, 34366, 55906, 44261], 'chosen_samples_score': ['0.96042866', '0.9639548', '0.98522264', '1.0367346', '1.0779443', '1.0384705', '0.9874913', '0.9789083', '0.9965612', '0.97655135']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.9654, 'nll': 0.32826384603977204}, 'chosen_samples': [42532, 49567, 32926, 31591, 48933, 12426, 57732, 28491, 36884, 37469], 'chosen_samples_score': ['1.0970395', '1.1022868', '1.1045531', '1.1851372', '1.170165', '1.221916', '1.1063058', '1.1057235', '1.2621796', '1.1361955']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9652, 'nll': 0.3161962628364563}, 'chosen_samples': [1455, 52048, 26266, 50431, 49188, 22083, 1075, 53736, 39116, 7851], 'chosen_samples_score': ['0.9760266', '0.9881654', '0.99169105', '0.99275464', '1.0145255', '1.0335889', '1.037017', '1.0499368', '1.0220675', '1.0825906']})
store['iterations'].append({'num_epochs': 15, 'test_metrics': {'accuracy': 0.9669, 'nll': 0.2977833166718483}, 'chosen_samples': [40726, 39146, 53640, 42353, 7833, 28734, 37161, 21896, 22139, 42161], 'chosen_samples_score': ['1.0546784', '1.0601286', '1.0872717', '1.0580885', '1.1106131', '1.2041845', '1.1206361', '1.1311097', '1.1881924', '1.1321158']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9632, 'nll': 0.307303424179554}, 'chosen_samples': [6704, 33752, 52092, 35246, 41464, 718, 50632, 14201, 30521, 54782], 'chosen_samples_score': ['0.97683656', '0.98031336', '0.9838607', '1.0163043', '1.0425432', '1.0487695', '1.1250353', '1.0226014', '0.9884138', '1.0197368']})
store['iterations'].append({'num_epochs': 14, 'test_metrics': {'accuracy': 0.9722, 'nll': 0.27029462456703185}, 'chosen_samples': [14406, 282, 6604, 18130, 13998, 14501, 57575, 53873, 52862, 20169], 'chosen_samples_score': ['0.9655262', '0.96761537', '0.9757861', '0.99949795', '0.9811497', '0.99631566', '0.9777123', '0.98081523', '1.0023', '1.2470726']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9651, 'nll': 0.32769377529621124}, 'chosen_samples': [36471, 57972, 8228, 4066, 32747, 32878, 21174, 20150, 2450, 52086], 'chosen_samples_score': ['0.91206515', '0.9131189', '0.9138404', '0.9145922', '0.93488336', '0.9354173', '0.9360186', '0.95784616', '0.96217215', '1.1194314']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9673, 'nll': 0.2968731090426445}, 'chosen_samples': [42136, 15948, 22607, 36866, 32880, 1121, 22497, 14896, 57728, 19642], 'chosen_samples_score': ['0.90941966', '0.9102266', '0.91300017', '0.92545027', '0.9285981', '0.94972414', '0.940984', '1.0282073', '0.95942837', '0.97052133']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9703, 'nll': 0.2882487788796425}, 'chosen_samples': [3392, 51764, 4030, 39600, 32108, 11616, 45658, 59343, 17603, 31594], 'chosen_samples_score': ['0.8947602', '0.9013608', '0.90844893', '0.9171951', '0.97332567', '0.94167763', '0.96502674', '0.9309908', '0.9513357', '0.9637691']})
store['iterations'].append({'num_epochs': 14, 'test_metrics': {'accuracy': 0.9681, 'nll': 0.28744699656963346}, 'chosen_samples': [50236, 14385, 18525, 32047, 42199, 56662, 30900, 50734, 54994, 15913], 'chosen_samples_score': ['0.9894397', '0.9921527', '1.000032', '1.0046401', '1.0149419', '1.015455', '1.0150805', '1.0265687', '1.0406651', '1.0886581']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9708, 'nll': 0.27013643831014633}, 'chosen_samples': [20869, 34942, 31094, 29744, 44753, 43126, 45024, 47479, 35406, 28368], 'chosen_samples_score': ['0.9298144', '0.93410176', '0.9315656', '0.93677545', '0.94097304', '0.9667934', '0.9970477', '0.96866596', '0.9508176', '0.94377244']})
store['iterations'].append({'num_epochs': 17, 'test_metrics': {'accuracy': 0.971, 'nll': 0.2856330633163452}, 'chosen_samples': [966, 48973, 12078, 35971, 57276, 41832, 12476, 49543, 46734, 59720], 'chosen_samples_score': ['1.0281953', '1.0339959', '1.0351455', '1.0427616', '1.0679629', '1.0746466', '1.0589366', '1.179233', '1.2259107', '1.0907532']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9723, 'nll': 0.2739916488528252}, 'chosen_samples': [13969, 16188, 21880, 32573, 37048, 54542, 32668, 54885, 21700, 43897], 'chosen_samples_score': ['0.9443941', '0.953039', '0.95399046', '0.9779675', '1.0152987', '0.98406196', '0.97270834', '0.9675868', '0.9695894', '1.0702078']})
store['iterations'].append({'num_epochs': 18, 'test_metrics': {'accuracy': 0.9724, 'nll': 0.27888427674770355}, 'chosen_samples': [47926, 32335, 43043, 11885, 51180, 9677, 17121, 42973, 45026, 35326], 'chosen_samples_score': ['1.008951', '1.0170033', '1.0154693', '1.0093422', '1.021126', '1.0391879', '1.0299034', '1.0705054', '1.1416199', '1.1334788']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9681, 'nll': 0.2914468988776207}, 'chosen_samples': [1149, 3676, 15402, 43198, 11711, 36268, 25192, 36714, 22633, 48154], 'chosen_samples_score': ['0.90103996', '0.9076192', '0.91145617', '0.93138564', '0.9372805', '0.9404236', '0.9520173', '1.0072052', '0.9758414', '0.95955586']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.9756, 'nll': 0.26279167681932447}, 'chosen_samples': [23674, 588, 32276, 7207, 50346, 9431, 8202, 11708, 28839, 5129], 'chosen_samples_score': ['0.9723571', '0.97319126', '0.97432107', '0.975812', '0.98159194', '1.0417719', '1.0942445', '1.063289', '1.0726066', '1.0847079']})
store['iterations'].append({'num_epochs': 18, 'test_metrics': {'accuracy': 0.9728, 'nll': 0.2737694948911667}, 'chosen_samples': [33389, 59297, 8509, 49515, 54628, 52808, 42078, 47297, 14790, 11292], 'chosen_samples_score': ['1.0019101', '1.0115883', '1.0132', '1.0189662', '1.0190816', '1.1511284', '1.0229135', '1.0496168', '1.0711153', '1.0302231']})
store['iterations'].append({'num_epochs': 19, 'test_metrics': {'accuracy': 0.974, 'nll': 0.2756667286157608}, 'chosen_samples': [54950, 23733, 32417, 33812, 18324, 45895, 52456, 20784, 55244, 14961], 'chosen_samples_score': ['1.0369382', '1.0438133', '1.0921359', '1.1789427', '1.1649959', '1.1552095', '1.0526049', '1.0491371', '1.0458008', '1.0921278']})
store['iterations'].append({'num_epochs': 14, 'test_metrics': {'accuracy': 0.9719, 'nll': 0.28214506804943085}, 'chosen_samples': [31413, 5315, 29803, 36072, 16190, 5103, 14697, 9774, 8978, 49192], 'chosen_samples_score': ['0.93312466', '0.9357318', '0.9531299', '0.9548854', '0.95733565', '0.9580642', '0.97871596', '0.9681197', '0.9650764', '0.9820922']})
store['iterations'].append({'num_epochs': 21, 'test_metrics': {'accuracy': 0.975, 'nll': 0.2684954062104225}, 'chosen_samples': [5042, 49890, 44442, 57882, 50454, 48382, 52968, 31530, 51964, 28030], 'chosen_samples_score': ['1.0145426', '1.0225194', '1.024088', '1.0287578', '1.1058059', '1.0482175', '1.0392334', '1.0808709', '1.0431674', '1.1215682']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.9711, 'nll': 0.2807793587446213}, 'chosen_samples': [2426, 59321, 30897, 45616, 29672, 11572, 10488, 14722, 12514, 24990], 'chosen_samples_score': ['0.9380891', '0.9968295', '1.028946', '1.0993011', '0.9642319', '1.1095629', '0.9430391', '0.9663274', '0.9603759', '0.9436325']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.9745, 'nll': 0.2683038547635078}, 'chosen_samples': [27880, 37427, 17296, 3094, 45439, 22561, 1512, 55620, 11482, 8879], 'chosen_samples_score': ['0.9887478', '0.99785113', '0.99837923', '1.0003674', '1.000515', '1.0044534', '1.0049937', '1.0172244', '1.0397273', '1.0052705']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.9751, 'nll': 0.25974455922842027}, 'chosen_samples': [49624, 39822, 52914, 55278, 10736, 52661, 47220, 46714, 51698, 34847], 'chosen_samples_score': ['0.95205873', '0.9556057', '0.98444027', '0.9901072', '1.0867839', '0.9570177', '0.97046196', '0.96902925', '0.97399026', '1.0337849']})
store['iterations'].append({'num_epochs': 18, 'test_metrics': {'accuracy': 0.9735, 'nll': 0.26661858558654783}, 'chosen_samples': [48975, 38389, 41611, 59151, 5384, 27703, 50317, 21327, 40280, 6050], 'chosen_samples_score': ['0.98156786', '0.98286474', '0.99831337', '0.9880398', '1.012161', '1.0418974', '1.0890367', '1.0819144', '1.0223039', '1.024602']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.976, 'nll': 0.2580254107713699}, 'chosen_samples': [23962, 46254, 43532, 9687, 40654, 49487, 56014, 57742, 55294, 50054], 'chosen_samples_score': ['0.9182099', '0.93546325', '0.9495496', '0.9579056', '0.9440443', '0.9579065', '0.9610068', '0.96414554', '0.98634046', '1.0162237']})
store['iterations'].append({'num_epochs': 23, 'test_metrics': {'accuracy': 0.9773, 'nll': 0.24183690547943115}, 'chosen_samples': [2761, 12834, 28347, 17706, 56346, 36669, 29827, 3719, 35205, 20050], 'chosen_samples_score': ['0.99384767', '1.0209496', '1.0299888', '0.999677', '1.0545487', '1.0376718', '1.012974', '1.0350589', '1.0000637', '1.1344478']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.977, 'nll': 0.24586375504732133}, 'chosen_samples': [32002, 43648, 50916, 34328, 51863, 28844, 34406, 4646, 44040, 33150], 'chosen_samples_score': ['0.9217357', '0.92530745', '0.94520116', '0.98209953', '0.99300677', '0.95169586', '0.9463631', '0.9568182', '0.982929', '0.9630902']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.9747, 'nll': 0.24625174850225448}, 'chosen_samples': [5174, 49153, 49634, 3810, 52548, 32419, 53844, 49672, 17592, 39316], 'chosen_samples_score': ['0.9221413', '0.9277175', '0.9467113', '0.93038386', '0.962413', '0.98315316', '0.9918835', '1.0133592', '1.0821285', '1.0146842']})
store['iterations'].append({'num_epochs': 18, 'test_metrics': {'accuracy': 0.9766, 'nll': 0.24244938045740128}, 'chosen_samples': [340, 44732, 31954, 45944, 39778, 24560, 18904, 41933, 54858, 31609], 'chosen_samples_score': ['0.9138584', '0.9211876', '0.9971331', '0.96132594', '0.9622827', '1.0865108', '0.92931205', '1.0844684', '0.9538106', '0.92199236']})
store['iterations'].append({'num_epochs': 21, 'test_metrics': {'accuracy': 0.9768, 'nll': 0.2541703999042511}, 'chosen_samples': [49573, 14655, 5298, 49164, 31252, 788, 33224, 41426, 48966, 46368], 'chosen_samples_score': ['0.9567223', '0.9965759', '0.9740327', '0.9787127', '0.9754917', '0.9747877', '0.9945406', '0.9569498', '1.0383296', '1.1322083']})
store['iterations'].append({'num_epochs': 21, 'test_metrics': {'accuracy': 0.9754, 'nll': 0.24166882485151292}, 'chosen_samples': [50355, 25874, 26358, 50714, 48890, 20641, 39526, 39752, 29294, 38256], 'chosen_samples_score': ['0.9618729', '0.962328', '0.9624206', '0.9751241', '0.9790118', '1.0650241', '1.0524045', '0.9955448', '1.0357101', '1.037919']})
store['iterations'].append({'num_epochs': 18, 'test_metrics': {'accuracy': 0.9775, 'nll': 0.22130073308944703}, 'chosen_samples': [49416, 11196, 42450, 28988, 29002, 39482, 17663, 38252, 37147, 35632], 'chosen_samples_score': ['0.87487173', '0.8763755', '0.8863056', '0.8889357', '0.8894656', '0.8784213', '0.89011073', '0.9637282', '1.0008702', '0.9153166']})
store['iterations'].append({'num_epochs': 17, 'test_metrics': {'accuracy': 0.9785, 'nll': 0.21984439939260483}, 'chosen_samples': [53980, 13021, 48102, 58832, 4762, 21601, 38920, 52169, 36704, 51993], 'chosen_samples_score': ['0.888577', '0.88890237', '0.8961441', '0.8892097', '0.9173317', '0.9321959', '0.9706692', '1.054181', '0.9422934', '0.9619337']})
store['iterations'].append({'num_epochs': 22, 'test_metrics': {'accuracy': 0.9801, 'nll': 0.23147815763950347}, 'chosen_samples': [41540, 36047, 56324, 18247, 34839, 42428, 55792, 623, 52800, 55804], 'chosen_samples_score': ['0.978769', '0.9799773', '0.9972999', '0.9947487', '0.9801565', '0.98051125', '0.99905074', '1.0572653', '1.1458043', '1.0547838']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.9809, 'nll': 0.22680318504571914}, 'chosen_samples': [39877, 25879, 1160, 15737, 25873, 32445, 32507, 42438, 39355, 59286], 'chosen_samples_score': ['0.8965249', '0.90339255', '0.9945975', '0.92242664', '1.0100586', '1.0414431', '0.9095572', '0.98972106', '0.90173286', '0.90667117']})
store['iterations'].append({'num_epochs': 20, 'test_metrics': {'accuracy': 0.9753, 'nll': 0.23250542879104613}, 'chosen_samples': [59731, 47225, 32016, 15781, 2928, 45602, 22824, 37696, 19868, 30751], 'chosen_samples_score': ['0.9481283', '0.9535239', '0.9601387', '0.97531945', '0.988353', '1.0696994', '1.0159751', '1.0187443', '1.0896366', '0.9915545']})
store['iterations'].append({'num_epochs': 19, 'test_metrics': {'accuracy': 0.9761, 'nll': 0.23801100701093675}, 'chosen_samples': [56586, 28932, 27296, 41295, 25917, 40022, 11296, 7125, 15743, 54097], 'chosen_samples_score': ['0.881579', '0.8866114', '0.89970326', '0.90780336', '0.91357005', '0.95511955', '0.97515154', '0.9474066', '0.9358688', '0.9202337']})
store['iterations'].append({'num_epochs': 23, 'test_metrics': {'accuracy': 0.9805, 'nll': 0.21938600093126298}, 'chosen_samples': [517, 45005, 17091, 14373, 53997, 32250, 37450, 46122, 9641, 34771], 'chosen_samples_score': ['0.9296426', '0.9329359', '0.9882772', '1.0492511', '0.961256', '1.0279765', '0.9923907', '1.0639458', '0.9605457', '0.9639916']})
store['iterations'].append({'num_epochs': 20, 'test_metrics': {'accuracy': 0.9768, 'nll': 0.23854516595602035}, 'chosen_samples': [9220, 50308, 80, 11074, 33426, 49892, 27176, 50462, 17817, 37906], 'chosen_samples_score': ['0.8956998', '1.002275', '0.90185267', '0.90502936', '0.9796144', '0.9158069', '1.057519', '0.92011636', '0.91640157', '0.906471']})
store['iterations'].append({'num_epochs': 17, 'test_metrics': {'accuracy': 0.9762, 'nll': 0.2326830044388771}, 'chosen_samples': [2831, 37704, 46432, 29903, 13428, 38932, 17466, 25055, 20792, 40466], 'chosen_samples_score': ['0.88349503', '0.88482535', '0.8945682', '0.91806173', '0.8990413', '0.9223134', '1.0085475', '0.9526239', '1.002945', '1.1344723']})
store['iterations'].append({'num_epochs': 19, 'test_metrics': {'accuracy': 0.9802, 'nll': 0.21346043795347214}, 'chosen_samples': [13945, 52236, 52834, 3798, 7074, 59719, 38408, 3030, 16572, 8297], 'chosen_samples_score': ['0.8949288', '1.0265031', '1.0126231', '0.97625047', '0.9003611', '0.93779933', '0.95231205', '1.0487199', '0.89684916', '0.8966093']})
store['iterations'].append({'num_epochs': 18, 'test_metrics': {'accuracy': 0.9812, 'nll': 0.2187278911471367}, 'chosen_samples': [48762, 21335, 58560, 42472, 47936, 31710, 23008, 10315, 8670, 56914], 'chosen_samples_score': ['0.8839383', '0.88530946', '0.89466995', '0.898258', '0.9113557', '0.8984284', '0.92657167', '0.9281078', '0.9398584', '1.0366081']})
store['iterations'].append({'num_epochs': 21, 'test_metrics': {'accuracy': 0.9826, 'nll': 0.20527199804782867}, 'chosen_samples': [55758, 52138, 12702, 50086, 1612, 10064, 54756, 991, 5216, 49010], 'chosen_samples_score': ['0.9086532', '0.9092301', '0.92556393', '0.9462639', '0.94865304', '0.9360485', '0.9291795', '0.9745586', '0.97974515', '0.97467035']})
store['iterations'].append({'num_epochs': 24, 'test_metrics': {'accuracy': 0.9799, 'nll': 0.2232593208551407}, 'chosen_samples': [1541, 1568, 17876, 7259, 24630, 8865, 5155, 18003, 22832, 41789], 'chosen_samples_score': ['0.94452524', '0.95411533', '0.9543352', '0.95950985', '0.9598229', '0.9846722', '1.0573109', '1.03946', '0.99207264', '0.9887891']})
store['iterations'].append({'num_epochs': 24, 'test_metrics': {'accuracy': 0.9764, 'nll': 0.2567752033472061}, 'chosen_samples': [2148, 55148, 46658, 7638, 10716, 1618, 51600, 8867, 22193, 55754], 'chosen_samples_score': ['0.95295066', '0.95826423', '0.96300536', '0.9675372', '0.9691363', '1.0596828', '0.98319817', '1.0115261', '1.0621222', '0.9730274']})
store['iterations'].append({'num_epochs': 22, 'test_metrics': {'accuracy': 0.9801, 'nll': 0.2281808853149414}, 'chosen_samples': [54880, 34610, 17005, 8300, 4529, 19866, 9180, 21150, 39561, 51054], 'chosen_samples_score': ['0.91465825', '0.92201865', '0.94252825', '0.95710886', '0.9466292', '0.97304994', '0.97320366', '1.0228826', '0.9880694', '0.99173295']})
store['iterations'].append({'num_epochs': 20, 'test_metrics': {'accuracy': 0.981, 'nll': 0.21797799915075303}, 'chosen_samples': [20720, 11643, 11797, 39311, 10982, 35926, 8453, 57718, 57663, 48137], 'chosen_samples_score': ['0.88084215', '0.8813928', '0.8823149', '0.933602', '0.9275548', '0.88496184', '0.9006676', '0.9052425', '0.89467895', '0.9345751']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.981, 'nll': 0.21718545407056808}, 'chosen_samples': [7058, 16836, 21598, 47912, 52713, 49064, 48397, 44570, 33552, 27952], 'chosen_samples_score': ['0.8394348', '0.8411575', '0.84532577', '0.851181', '0.85368955', '0.8566897', '0.8567649', '0.91992295', '0.9891225', '0.8646983']})
store['iterations'].append({'num_epochs': 18, 'test_metrics': {'accuracy': 0.9825, 'nll': 0.21751808375120163}, 'chosen_samples': [635, 40824, 19395, 52516, 46248, 36363, 5554, 49541, 54848, 28633], 'chosen_samples_score': ['0.88854015', '0.8990873', '0.9087124', '0.91587675', '0.93181646', '0.94614094', '0.9264753', '0.93730783', '1.0322887', '0.94477016']})
store['iterations'].append({'num_epochs': 18, 'test_metrics': {'accuracy': 0.9773, 'nll': 0.21880699694156647}, 'chosen_samples': [48899, 52927, 51986, 10070, 24052, 4784, 17494, 13878, 47285, 23886], 'chosen_samples_score': ['0.8129922', '0.8151596', '0.81628084', '0.8237534', '0.82840645', '0.8366572', '0.8577933', '0.8933516', '0.8599431', '0.8740966']})
store['iterations'].append({'num_epochs': 26, 'test_metrics': {'accuracy': 0.9814, 'nll': 0.2131793662905693}, 'chosen_samples': [20120, 34872, 34500, 22470, 17079, 24078, 28392, 27429, 31637, 11823], 'chosen_samples_score': ['0.90361834', '0.9064703', '0.93018866', '0.92361635', '0.93448097', '0.93555224', '0.93880475', '0.95115805', '1.0458854', '1.0234787']})
store['iterations'].append({'num_epochs': 23, 'test_metrics': {'accuracy': 0.9823, 'nll': 0.1986723117530346}, 'chosen_samples': [44670, 50576, 50930, 12123, 33162, 49616, 34968, 52060, 8093, 45917], 'chosen_samples_score': ['0.8808986', '0.8822812', '0.95987225', '0.9318909', '1.172279', '0.8826684', '0.9156105', '0.89352125', '0.9492033', '0.8842254']})
store['iterations'].append({'num_epochs': 22, 'test_metrics': {'accuracy': 0.9823, 'nll': 0.20078423917293547}, 'chosen_samples': [7920, 30130, 2302, 30692, 47515, 50994, 1033, 49282, 5798, 2618], 'chosen_samples_score': ['0.9057857', '0.9088802', '0.9184522', '0.99425805', '1.0243362', '0.9223889', '0.9518111', '0.9096019', '1.0557115', '0.93164146']})
store['iterations'].append({'num_epochs': 21, 'test_metrics': {'accuracy': 0.9813, 'nll': 0.20330493226647378}, 'chosen_samples': [11007, 28357, 1032, 16011, 49274, 38219, 15412, 23486, 30952, 47680], 'chosen_samples_score': ['0.8491657', '0.85575545', '0.86573213', '1.0233252', '0.9114218', '0.87002134', '0.87356955', '0.8878931', '0.9976363', '0.88485247']})
store['iterations'].append({'num_epochs': 24, 'test_metrics': {'accuracy': 0.9818, 'nll': 0.21562371104955674}, 'chosen_samples': [44712, 45502, 50841, 34916, 26588, 47949, 52246, 8480, 8883, 50091], 'chosen_samples_score': ['0.90699214', '0.918876', '0.9234118', '0.94438446', '0.9440874', '0.93997234', '0.9451258', '0.9508704', '0.9467248', '1.0050056']})
store['iterations'].append({'num_epochs': 28, 'test_metrics': {'accuracy': 0.9823, 'nll': 0.1913700021803379}, 'chosen_samples': [49889, 25300, 57523, 30123, 26405, 32814, 47560, 49555, 22200, 21134], 'chosen_samples_score': ['0.929865', '0.9376593', '0.99696606', '0.9556495', '0.95834744', '0.98236275', '0.9460501', '1.0028267', '0.9938807', '1.0001609']})
store['iterations'].append({'num_epochs': 26, 'test_metrics': {'accuracy': 0.9802, 'nll': 0.21344402432441711}, 'chosen_samples': [50417, 11880, 32426, 8459, 15699, 57931, 38142, 11647, 30818, 14972], 'chosen_samples_score': ['0.91626817', '0.9167625', '0.9611998', '0.9301421', '0.9242769', '0.9663918', '0.9275627', '0.9765466', '1.1037245', '0.97954226']})
store['iterations'].append({'num_epochs': 30, 'test_metrics': {'accuracy': 0.9842, 'nll': 0.19944047257304193}, 'chosen_samples': [29952, 36610, 57486, 56939, 470, 56992, 14765, 4475, 41034, 12651], 'chosen_samples_score': ['0.94192034', '0.95956945', '0.9622193', '0.9717092', '1.0508062', '0.9678379', '0.9689264', '0.97499573', '1.0189823', '0.96418583']})
store['iterations'].append({'num_epochs': 21, 'test_metrics': {'accuracy': 0.9815, 'nll': 0.19137457013130188}, 'chosen_samples': [46247, 45462, 1015, 41299, 15518, 33995, 58812, 17420, 49474, 1175], 'chosen_samples_score': ['0.86098987', '0.8666458', '0.8877549', '0.9004263', '0.94386667', '0.87661964', '0.8828879', '0.890134', '0.9240626', '0.8745614']})
store['iterations'].append({'num_epochs': 29, 'test_metrics': {'accuracy': 0.9837, 'nll': 0.18722183629870415}, 'chosen_samples': [50946, 51337, 31626, 15106, 29594, 18382, 17749, 37295, 892, 20976], 'chosen_samples_score': ['0.89713997', '0.89985806', '0.8986801', '0.9033179', '0.90927213', '0.9146749', '0.9153213', '0.9163971', '0.97029763', '0.92524844']})
store['iterations'].append({'num_epochs': 24, 'test_metrics': {'accuracy': 0.9819, 'nll': 0.2071329116821289}, 'chosen_samples': [57764, 8680, 49067, 25159, 45761, 34648, 21058, 35482, 49084, 21900], 'chosen_samples_score': ['0.8653955', '0.86603343', '0.8717433', '0.87466556', '0.8916658', '0.9703022', '0.88713056', '0.94161654', '0.8977488', '0.9010249']})
store['iterations'].append({'num_epochs': 20, 'test_metrics': {'accuracy': 0.9834, 'nll': 0.19796403422951697}, 'chosen_samples': [14283, 14290, 56228, 25546, 34678, 29185, 24803, 53102, 32018, 25586], 'chosen_samples_score': ['0.80688846', '0.8074722', '0.81235546', '0.83948815', '0.8140736', '0.826303', '0.8402368', '0.8863959', '0.86170864', '0.86740786']})
store['iterations'].append({'num_epochs': 21, 'test_metrics': {'accuracy': 0.9826, 'nll': 0.19162499010562897}, 'chosen_samples': [50897, 228, 38315, 43943, 854, 47999, 22816, 31672, 56292, 26722], 'chosen_samples_score': ['0.817291', '0.82555246', '0.81735057', '0.83125985', '0.8403825', '0.8499242', '0.87229264', '0.8351946', '0.84388655', '0.90820086']})
store['iterations'].append({'num_epochs': 26, 'test_metrics': {'accuracy': 0.9811, 'nll': 0.20675649419426917}, 'chosen_samples': [49895, 7732, 28710, 48010, 31301, 29704, 18572, 24934, 5408, 31738], 'chosen_samples_score': ['0.8581944', '0.85895663', '0.8641151', '0.8769148', '0.91695607', '0.96794325', '0.9606201', '0.9207202', '0.8916391', '0.92044497']})
store['iterations'].append({'num_epochs': 30, 'test_metrics': {'accuracy': 0.9836, 'nll': 0.19093263819813727}, 'chosen_samples': [3762, 148, 36141, 17540, 8200, 50090, 15725, 4822, 49501, 43745], 'chosen_samples_score': ['0.95998144', '0.9622531', '0.97747636', '0.98733175', '0.97738254', '1.0189949', '1.0464604', '1.0866572', '1.0268105', '1.0335433']})
store['iterations'].append({'num_epochs': 23, 'test_metrics': {'accuracy': 0.9824, 'nll': 0.1853531464934349}, 'chosen_samples': [6251, 41334, 31706, 23956, 8226, 4237, 8821, 50753, 5630, 47475], 'chosen_samples_score': ['0.8463836', '0.8477376', '0.86087763', '0.8893514', '0.86765546', '0.8820631', '0.90323937', '0.9129684', '0.9523588', '0.9438984']})
store['iterations'].append({'num_epochs': 25, 'test_metrics': {'accuracy': 0.9826, 'nll': 0.1899605095386505}, 'chosen_samples': [35401, 13376, 55496, 5052, 10124, 48297, 7832, 23140, 19714, 49082], 'chosen_samples_score': ['0.8518768', '0.8531501', '0.86996925', '0.8825555', '0.93121713', '0.9100712', '0.948418', '0.91902167', '0.90138775', '0.9981641']})
store['iterations'].append({'num_epochs': 30, 'test_metrics': {'accuracy': 0.9854, 'nll': 0.17097146660089493}, 'chosen_samples': [24512, 31748, 6418, 47549, 51466, 34448, 59681, 18504, 11777, 440], 'chosen_samples_score': ['0.9264216', '0.9353672', '0.9524996', '0.93617755', '0.9471693', '0.95391726', '0.9814734', '0.95451796', '0.97613657', '1.1522002']})
store['iterations'].append({'num_epochs': 25, 'test_metrics': {'accuracy': 0.9848, 'nll': 0.17417498156428338}, 'chosen_samples': [5194, 40530, 20446, 8761, 9552, 29320, 19590, 16676, 46887, 8704], 'chosen_samples_score': ['0.85257995', '0.87019044', '0.87275195', '0.90108544', '0.90591055', '0.95913523', '0.8738325', '0.87413865', '0.9333375', '0.91402817']})
store['iterations'].append({'num_epochs': 28, 'test_metrics': {'accuracy': 0.9819, 'nll': 0.18096225410699845}, 'chosen_samples': [14152, 33425, 15574, 31576, 10217, 16488, 5936, 41453, 14690, 43560], 'chosen_samples_score': ['0.8498325', '0.87189394', '0.87841964', '0.88075435', '0.88517356', '0.88744867', '0.91195005', '0.94212115', '0.9145182', '0.99703705']})
store['iterations'].append({'num_epochs': 23, 'test_metrics': {'accuracy': 0.986, 'nll': 0.1783410392701626}, 'chosen_samples': [20230, 6848, 55190, 52006, 43434, 6905, 20660, 27653, 17309, 37552], 'chosen_samples_score': ['0.82003075', '0.82119596', '0.869826', '0.8917357', '0.9634456', '0.9562116', '0.849278', '0.85552245', '0.8288583', '0.82304215']})
store['iterations'].append({'num_epochs': 21, 'test_metrics': {'accuracy': 0.9859, 'nll': 0.18215135410428046}, 'chosen_samples': [48649, 5616, 23824, 20036, 3220, 31512, 10894, 16939, 50514, 38158], 'chosen_samples_score': ['0.80739987', '0.8138454', '0.81548816', '0.8261797', '0.84303', '0.8541808', '1.0047395', '0.8873114', '0.90206283', '0.8596684']})
store['iterations'].append({'num_epochs': 25, 'test_metrics': {'accuracy': 0.9852, 'nll': 0.16820862144231796}, 'chosen_samples': [35324, 43686, 5679, 39516, 6347, 4050, 47247, 43575, 16022, 11767], 'chosen_samples_score': ['0.83906966', '0.83948594', '0.8521185', '0.84965575', '0.8571624', '0.8406761', '0.89249957', '0.9163648', '0.9187848', '0.9299168']})
